14 Prevalence of Mid-Range Visual Functions and their Relationship to Higher-order Visual Functions after Stroke
Bibliographic record
Abstract
Objective: Visual deficits are common after stroke and are powerful predictors for the chronic functional outcome. However, while basic visual field and recognition deficits are relatively easy to assess with standardized methods, selective deficits in visual primitives, such as shape or motion, are harder to identify, as they often require a symmetrical bilateral posterior lesion in order to provoke full field deficits. We aimed to investigate the prevalence and co-occurrence of hemifield “mid-range” visual deficits. In addition, we looked at the repercussions of these mid-range deficits on higher-order visual cognitive functions, such as visuoconstruction and memory. At a more theoretical level, we investigated whether associations between deficits in 'mid-range’ visual functions and deficits in higher-order visual cognitive functions are in line with a hierarchical, two-pathway model of the visual brain. Participants and Methods: In 220 stroke patients and a healthy control group (N=49), we assessed the perception of colour (isoluminant stimuli in the red-green range), shape (Efron shapes), location (dot in a circle), orientation (lines at different angles), contrast (bars with converging grey-level differences), texture (from Brodatz grayscale texture album) and correlated motion (different percentages of dots moving in the same direction). All tasks started with a fixation dot presented at the centre of the screen. After one second, a target stimulus was presented on the horizontal midline at either 5° to the left or at 5° to the right side of the fixation. Then, after 1.5 seconds, two response items appeared in addition to the target stimulus for three seconds. To control for eye movements, we used an eye-tracker to present the target in a gaze contingent fashion. Thus, the target always remained in the correct retinal position independent of eye movements. In a subset of 182 ischemic stroke patients, we also assessed visuoconstruction (Copy Rey-Complex Figure Test), visual emotion recognition (FEEST test) and visual memory (Doors-test). Results: The results showed that deficits in motion-perception were most prevalent (26%), followed by colour (22%), texture (22%), location (21%), orientation (18%), contrast (14%), shape (14%) and glossiness (13%). 63% of the stroke patients showed one or more mid-range visual deficits. Overlap of deficits was small; they mostly occurred in isolation or co-occurred with only one or two other deficits. Impairments in mid-range visual functions could not predict performance on higher-order visual cognitive tasks. Impaired visuoconstruction and visual memory were only modestly predicted by a worse location perception. Impaired emotion perception was modestly predicted by a worse orientation perception. In addition, double dissociations were found: there were patients with selective deficits in 'mid-range’ visual functions without higher-order visual deficits and vice versa. Conclusions: First, deficits in “mid-range” visual functions are very prevalent. Since we found no strong patterns of co-occurrences, we suggest that an assessment of deficits at this level of visual processing requires screening the full range of visual functions. Second, the relationship between mid-range visual tasks and higher-order visual cognitive tasks is weak. Finally, our findings are not supportive of the hierarchical, two-pathway model but more in line with an alternative patchwork model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".